Financial Distress Prediction with Stacking Ensemble Learning

Muhammad Fadhlil Hadi (Master Program in Computer Science, FMIPA UGM, Yogyakarta)
De-Ron Liang (National Central University, Zhongli)
Tri Kuntoro Priyambodo (Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta)
Azhari SN (Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Vol 16, No 3 (2022): July

Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of manipulation of financial statements. None of the previous studies combined the five Altman Ratios with the Beneish M-Score. We use Stacking Ensemble Learning to classify crisis companies and perform a comprehensive analysis. This insight helps the investment public make lending decisions by mixing all the financial indicator information and assessing it carefully based on long-term and short-term conditions and possible manipulation of financial statements.